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Using real estate occupancy data to reduce costs, mitigate risks and meet business needs

Author

Listed:
  • Johnson, Jess Joaquin
  • Duncan, Ann
  • Adams, Steven

Abstract

Confronting a dynamic business environment in which all expense-related decisions are under increased scrutiny, innovative CRE managers are collecting data on the actual use of real estate to improve decision-making, measure the performance of real estate assets and identify opportunities to reduce costs. However, translating this data into actionable knowledge requires sound methods of collection and analysis in order to build credibility with stakeholders throughout the organisation and in the C-suite.Through the collaboration between Vertical Integration, Inc, and PricewaterhouseCoopers LLP (PwC), this paper illustrates how big data can be effectively leveraged to reduce real estate costs, ensure real estate resources meet business needs, and mitigate operational risks. Utilising methods such as regression analysis, seasonality adjustments, and Monte Carlo modelling, a data-driven, fact-based approach is applied to corporate real estate's most common challenges, including supporting alternative work strategies, accommodating employees acquired through corporate acquisitions, and supporting the dynamic needs of an increasingly mobile workforce.

Suggested Citation

  • Johnson, Jess Joaquin & Duncan, Ann & Adams, Steven, 2011. "Using real estate occupancy data to reduce costs, mitigate risks and meet business needs," Corporate Real Estate Journal, Henry Stewart Publications, vol. 2(1), pages 28-42, September.
    • Handle: RePEc:aza:crej00:y:2011:v:2:i:1:p:28-42
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    More about this item

    Keywords

    occupancy; demand; occupancy data; portfolio optimisation; actual use data; Monte Carlo;
    All these keywords.

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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